title: Modification and fixed-point analysis of a Kalman filter for orientation estimation based on 9D inertial measurement unit data.
abstract: A common approach for high accuracy sensor fusion based on 9D inertial measurement unit data is Kalman filtering. State of the art floating-point filter algorithms differ in their computational complexity nevertheless, real-time operation on a low-power microcontroller at high sampling rates is not possible. This work presents algorithmic modifications to reduce the computational demands of a two-step minimum order Kalman filter. Furthermore, the required bit-width of a fixed-point filter version is explored. For evaluation real-world data captured using an Xsens MTx inertial sensor is used. Changes in computational latency and orientation estimation accuracy due to the proposed algorithmic modifications and fixed-point number representation are evaluated in detail on a variety of processing platforms enabling on-board processing on wearable sensor platforms.
authors.forename: Hans-Peter
authors.initials: HP
authors.lastname: Brückner
authors.forename: Christian
authors.initials: C
authors.lastname: Spindeldreier
authors.forename: Holger
authors.initials: H
authors.lastname: Blume
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D009949:Orientation; D012815:Signal Processing, Computer-Assisted